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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

VERA Integration in the NEAMS Workbench

The Virtual Environment for Reactor Applications (VERA) is a cutting edge simulation code suite used to solve complex multiphysics problems for nuclear reactors. VERA makes use of high performance computing (HPC) systems to solve large problems that were not historically tractable. While extensive work has been put into the usability of VERA, it still takes significant effort to build reactor models, ensure their correctness, properly execute the calculations, and conduct the necessary analysis of the results. This difficulty is common for many complex codes such as VERA. As part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the NEAMS Workbench has been developed and maintained. The goal of the NEAMS Workbench is to provide a single interface with which to execute a wide variety of physics codes, from building the input models to analyzing the results. This document serves as a manual detailing the current integration of VERA with the NEAMS Workbench and a guide for configuring Workbench to allow remote execution of VERA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRSDate not supplied

DLES Unreal Simulation Tool (DUST)

NASA’s future Artemis missions to the Moon seek to explore areas around the Lunar South Pole. Though humans have previously set foot on the lunar surface, the proposed region provides unique and challenging environments that require insight and investigation prior to arrival. Several teams throughout the agency are performing this site and mission planning, design, and analysis to support areas like the Human Landing System (HLS), surface mobility, habitation elements, and scientific exploration. The NASA Exploration Systems Simulation (NExSyS) team at Johnson Space Center is developing a graphical environment of the Lunar South Pole region. Lunar terrain information collected from the Lunar Reconnaissance Orbiter (LRO) is compiled and made available through Johnson Space Center’s Digital Lunar Exploration Sites (DLES) data sets. The DLES data is used to build this graphic environment. The process of ingesting and accurately modeling this information in a meaningful way for analysis creates its own challenges such as generating a performant model from the source data and the application of curvature. Additionally, the area around the Lunar South Pole experiences different lighting conditions than those observed from the Apollo missions. The need to use the lunar environmental data products provided by DLES combined with the capability to calculate date specific ephemerides in real-time has given rise to the development of the DLES Unreal Simulation Tool (DUST). DUST incorporates augmented terrain from the DLES product into a desktop application that allows exploration of the Lunar South Pole region and its complex lighting conditions. DUST leverages advanced capabilities in the recently released Unreal Engine 5 renderer by Epic Games such as double precision for positioning of planetary bodies and surface elements, multiple infinite light sources to represent the Sun and eventually Earthshine, high resolution shadow maps for dynamic shadow accuracy, real-time software ray-tracing for multi-surface bounce lighting to render sunlight reflected off surface elements and terrain features, and performance optimized level of detail shifting as the eyepoint changes in a scene. This paper details the DUST application, the technologies of the engine platform that enable scientific and engineering analysis, the unique techniques and processes developed to consume the DLES data sets, and how the tool is being used to support the Artemis program.

Lunar Visualization↗
DOE OSTI2025

Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window

ABSTRACT Cosmological hydrodynamical simulations provide valuable insights on galaxy evolution when coupled with observational data. Comparisons with real galaxies are typically performed via scaling relations of the observables. Here, we follow an alternative approach based on the spectral covariance in a model-independent way. We build upon previous work by Sharbaf et al. that studied the covariance of high-quality SDSS (Sloan Digital Sky Survey) continuum-subtracted spectra in a relatively narrow range of velocity dispersion ($\sigma \in [100,150]$ km s$^{-1}$). Here, the same analysis is applied to synthetic data from the eagle and IllustrisTNG100 simulations, to assess the ability of these runs to mimic real galaxies. The real and simulated spectra are consistent regarding spectral covariance, although with subtle differences that can inform the implementation of subgrid physics. Spectral fitting done a posteriori on stacks segregated with respect to latent space reveals that the first principal component (PC1) is predominantly influenced by the stellar age distribution, with an underlying age–metallicity degeneracy. Good agreement is found regarding star formation prescriptions but there is disagreement with active galactic nucleus (AGN) feedback, that also affects the subset of quiescent galaxies. We show a substantial difference in the implementation of the AGN subgrid prescriptions, regarding central black hole seeding, that could lead to the mismatch. Differences are manifest between these two simulations in the star formation histories stacked with respect to latent space. We emphasize that this methodology only relies on the spectral covariance to assess whether simulations provide a true representation of galaxy formation.

Sharbaf, Z. (ORCID:0009000450545946)↗
DOE OSTI2026

Exploring Building Retrofit Strategies Using AutoBEM Under Future Weather Scenarios

This study evaluates the long-term effectiveness of energy conservation measures (ECMs) on building energy consumption using AutoBEM, a scalable modeling framework driven by the high-resolution Model America dataset. We simulated 18,951 buildings in Flagstaff, Arizona under four climate scenarios using Future Typical Meteorological Year (fTMY) weather files for six time periods spanning from 1980 to 2099. Six ECMs were analyzed across electricity and gas usage, including HVAC fuel-switching, insulation upgrades, and infiltration control. While some measures, such as reducing space infiltration by percentage, showed minimal or even negative impact on total energy savings at the aggregate level, they proved highly effective for specific building types. Conversely, HVAC electrification offers high gas reduction but shifts demand to electricity, highlighting critical trade-offs under different climate trajectories. Building-type-specific analysis under SSP5-RCP8.5 (2080–2099) revealed significant variation in ECM performance, underscoring the need for targeted retrofit strategies. This study demonstrates the power of combining fTMY projections with large-scale simulations to inform data-driven retrofit planning.

Chowdhury, Shovan [ORNL]↗
NASA NTRS1974

The NASA Langley building solar project and the supporting Lewis solar technology program

A solar energy technology program is described that includes solar collector testing in an indoor solar simulator facility and in an outdoor test facility, property measurements of solar panel coatings, and operation of a laboratory-scale solar model system test facility. Early results from simulator tests indicate that non-selective coatings behave more nearly in accord with predicted performance than do selective coatings. Initial experiments on the decay rate of thermally stratified hot water in a storage tank have been run. Results suggest that where high temperature water is required, excess solar energy collected by a building solar system should be stored overnight in the form of chilled water rather than hot water.

Ragsdale, R. G.↗
DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗
DOE OSTI2021

Exascale models of stellar explosions: Quintessential multi-physics simulation

The ExaStar project aims to deliver an efficient, versatile, and portable software ecosystem for multi-physics astrophysics simulations run on exascale machines. The code suite is a component-based multi-physics toolkit, built on the capabilities of current simulation codes (in particular Flash-X and Castro), and based on the massively parallel adaptive mesh refinement framework AMReX. It includes modules for hydrodynamics, advanced radiation transport, thermonuclear kinetics, and nuclear microphysics. The code will reach exascale efficiency by building upon current multi- and many-core packages integrated into an orchestration system that uses a combination of configuration tools, code translators, and a domain-specific asynchronous runtime to manage performance across a range of platform architectures. The target science includes multi-physics simulations of astrophysical explosions (such as supernovae and neutron star mergers) to understand the cosmic origin of the elements and the fundamental physics of matter and neutrinos under extreme conditions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An open-source framework for simulation-based testing of buildings control strategies

Here this paper presents a simulation framework for evaluating building control strategies (BCSs), developed with VOLTTRON, an opensource platform that integrates data, devices, and systems for sensing and control applications, and is based on co-simulation interfaces. It realizes an integrated environment for both testing and deploying BCSs, thereby eliminating the need for having a dedicated implementation of BCS for testing. For the first time, this framework provides critical functionalities, including scalable communication management and time-drive simulation advance, i.e., advancing the simulation based on clock time. We applied this framework to evaluating two BCSs from ASHRAE Guideline 36-2008 and ASHRAE Standard 90.1-2019. The evaluation results show that those BCSs primarily benefit the heating operation by reducing gas usage but yield insignificant savings in electricity consumption. The results also emphasize the importance of tuning the parameters of the BCSs to achieve better performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Digitization modeling of a CGEM detector based on Garfield++ simulation

Abstract Background The Cylindrical Gas Electron Multiplier Inner Tracker (CGEM-IT) is one of the candidates for the replacement of the current BESIII inner drift chamber, which has developed serious aging effects due to beam-related backgrounds. Purpose To build a full digitization model, it is necessary to understand the electron drift behavior and signal induction. Methods A simulation study based on Garfield++ and ANSYS software has been performed. Results Parameters describing the Lorentz angle, diffusion effect, drift time, multiplication, and induction are obtained from the Garfield++ simulation. Based on these results, a preliminary digitization model is implemented in the BESIII offline software system.

Zhao, Jing-Yi (ORCID:0000000220287286)↗
DOE OSTI2025

Enhancing EnergyPlus capabilities to model dynamic building envelopes using python plugin

Nearly half of the energy consumption in the United States is related to buildings, resulting in an urgent need to develop innovative technologies to improve building energy efficiency. Dynamic building envelopes, comprising switchable insulation and thermal energy storage materials, have been proposed recently as a promising solution to reduce buildings' heating and cooling loads by thermally coupling the indoor environment with the ambient environment when beneficial while decoupling them when outdoor conditions are not favorable. Although various related technologies are still underway, the whole-building energy modeling tools, like EnergyPlus, do not have the capability to simulate the transient and dynamic nature of dynamic envelope materials and components to accurately predict their impact on building energy use. The objective of this study is to formulate a method in EnergyPlus simulation engine to model multilayer envelopes, comprising dynamic building materials with variable thermophysical properties, and discuss the changes made to the program using a Python plugin. Furthermore, the thermal performance of the dynamic envelopes using the proposed method is compared and verified with the results from a well-established commercial code, COMSOL Multiphysics. A parametric assessment is also conducted to evaluate the energy efficiency benefits of dynamic envelopes in a single-family residential building, demonstrating total annual energy savings up to 11.6 %, when a dynamic envelope operates alone, and up to 18.2 % when it is combined with a thin layer of phase change material as a thermal storage medium. Finally, a United States wide energy efficiency assessment is presented to showcase the geographical spread of the energy savings. The method designed and implemented in this study provides the researchers with the ability to implement their dynamic insulation methods in EnergyPlus and evaluate the whole building energy impact.

25 ENERGY STORAGE↗
DOE OSTI2021

Tools for Assessing Performance Project: FY2021 Quarter 4 Report

The rotatable building located at Texas Tech’s Reese Technology Center is approximately 14 m wide (width being defined as more normal to the wind than parallel), 9 m long (aligned more with the wind than perpendicular), and 4 m tall. By placing 29 sonic anemometers downwind of the building, see Fig. 1, this facility provided a chance to collect data concerning both the wake velocity deficit distribution in the downstream and crosssteam directions (relative to the mean wind) behind an isolated building. A preliminary comparison between this data and the recently- developed fast-running diffusive wake model, which was developed based on wind tunnel and LES (performed with JOULES) simulations, for the purpose of either validating this model of understanding potential persistent differences between the idealized wind tunnel or LES conditions and those of full-scale real world phenomena. For this preliminary exploration, we utilized the diffusive wake model implemented in the QUIC model.

42 ENGINEERING↗
DOE OSTI2025

Optimization Through Multi-Fidelity Modeling

We present a novel method for optimizing parameter selection for simulations with an evaluation budget. We start with an existing method for building a multi-fidelity model out of many low-fidelity simulations and few high-fidelity simulations. We propose a novel method to simplify parameter selection without sacrificing performance. We verify these results and compare with existing literature. Next, we propose a novel algorithm which uses this difference model to suggest new points in the parameter design space to simulate. We add each point we simulate to the model to improve its quality for the next iteration. The algorithm trades off reducing the uncertainty of the existing model with optimization of the objective. The first is more useful when a large fraction of the computation budget remains. The second is more useful when a small fraction of the computation budget remains. Our method converges to the optimum by using a high-fidelity evaluation for just 16 of the 427 points. Our method is general enough to work if there is no low-fidelity model. Furthermore, it is agnostic to the underlying physics of the problem. Therefore, both the low-fidelity and high-fidelity models can be generated by any arbitrary function, including simulations and physical experiments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Wear-Out Sensitivity Analysis Project Abstract

During the course of the Summer 2015 internship session, I worked in the Reliability and Maintainability group of the ISS Safety and Mission Assurance department. My project was a statistical analysis of how sensitive ORU's (Orbital Replacement Units) are to a reliability parameter called the wear-out characteristic. The intended goal of this was to determine a worst case scenario of how many spares would be needed if multiple systems started exhibiting wear-out characteristics simultaneously. The goal was also to determine which parts would be most likely to do so. In order to do this, my duties were to take historical data of operational times and failure times of these ORU's and use them to build predictive models of failure using probability distribution functions, mainly the Weibull distribution. Then, I ran Monte Carlo Simulations to see how an entire population of these components would perform. From here, my final duty was to vary the wear-out characteristic from the intrinsic value, to extremely high wear-out values and determine how much the probability of sufficiency of the population would shift. This was done for around 30 different ORU populations on board the ISS.

Harris, Adam↗
NASA NTRSDate not supplied

A Physics-Based Spatial Wake Interactional Model of Fixed-Wing Aircraft and Rotorcraft for Urban Air Mobility

This paper presents a study of a physics-based spatial wake interactional model for urban air mobility. A modeling method for spatial wake interaction of fixed-wing aircraft and rotorcraft is presented. The method validates an existing wake vortex model with flight data from the existing literature. The model is then adopted for both fixed-wing aircraft and rotorcraft. A lift circulation model is developed for rotorcraft to account for unsteady aerodynamics and the compressibility effect. This lift circulation model provides more realistic estimate of lift circulation for rotorcraft for used in wake vortex modeling. A vehicle-building wake interaction is studied using the method of image. The effects of wake vortex encounters on aerodynamic loads, stability, and control are examined. A flight control simulation is performed to demonstrate recovery during a simulated wake vortex encounter.

Wake Vortex Encounter↗
DOE OSTI2024

Nova Analysis: Holistically Valuing the Contributions of Residential Efficiency, Solar and Storage

Policies to address climate change and grid modernization, in combination with cost reductions and technological advancements in energy efficiency (EE) and distributed energy resources (DER), are driving rapid deployment of building electrification and energy efficiency retrofits, rooftop solar photovoltaics (PV), smart thermostats, smart water heaters, and battery energy storage. In residential buildings, there are multiple stakeholders (occupant, utility, aggregator, society at large) that are each focused on different value streams. This project uses a suite of metrics intended to all of these value streams to try to holistically analyze the benefits that come from solar, storage, and energy efficiency. To demonstrate the value of these metrics, a semi annual study was performed simulating hundreds of homes across the U.S. under several different upgrade scenarios.

14 SOLAR ENERGY↗
DOE OSTI2023

Sensor Incipient Fault Impacts on Building Energy Performance: A Case Study on a Multi-Zone Commercial Building

Existing studies show sensor faults/error could double building energy consumption and carbon emissions compared with the baseline. Those studies assume that the sensor error is fixed or constant. However, sensor faults are incipient in real conditions and there were extremely limited studies investigating the incipient sensor fault impacts systematically. This study filled in this research gap by studying time-developing sensor fault impacts to rule-based controls on a 10-zone office building. The control sequences for variable air volume boxes (VAV) with an air handling unit (AHU) system were selected based on ASHRAE Guideline 36-2018: High-Performance Sequences of Operation for HVAC Systems. Large-scale simulations on cloud were conducted (3600 cases) through stochastic approach. Results show (1) The site energy differences could go –3.3% lower or 18.1% higher, compared with baseline. (2) The heating energy differences could go –66.5% lower or 314.4% higher, compared with baseline. (3) The cooling energy differences could go –11.5% lower or 65.0% higher, compared with baseline. (4) The fan energy differences could go 0.15% lower or 6.9% higher, compared with baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗
DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗